1.山东大学机械工程学院高效洁净机械制造教育部重点实验室 济南 250061; 2.山东大学机械工程国家级实验教学示范中心 济南 250061; 3.山东大学工程训练中心 济南 250002
TP391.41
山东省重点研发计划（2020JMRH0202）、山东省新旧动能转换重大产业攻关项目（202113）、济宁市重点研发计划项目（2021DZP005）、山东大学教育教学改革研究项目(2022Y133，2022Y124，2022Y312)、山东省重点研发计划(重大科技创新工程)（2022CXGC020701）项目资助
Zhou Bo
1.Key Laboratory of HighEfficiency and Clean Mechanical Manufacture,School of Mechanical Engineering,Shandong University， Jinan 250061,China； 2.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University，Jinan 250061,China； 3.Engineering Training Center, Shandong University, Jinan 250002, ChinaWan Yi
1.Key Laboratory of HighEfficiency and Clean Mechanical Manufacture,School of Mechanical Engineering,Shandong University， Jinan 250061,China； 2.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University，Jinan 250061,China； 3.Engineering Training Center, Shandong University, Jinan 250002, ChinaLiang Xichang
1.Key Laboratory of HighEfficiency and Clean Mechanical Manufacture,School of Mechanical Engineering,Shandong University， Jinan 250061,China； 2.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University，Jinan 250061,China； 3.Engineering Training Center, Shandong University, Jinan 250002, ChinaHou Jiarui
1.Key Laboratory of HighEfficiency and Clean Mechanical Manufacture,School of Mechanical Engineering,Shandong University， Jinan 250061,China； 2.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University，Jinan 250061,China； 3.Engineering Training Center, Shandong University, Jinan 250002, China1.Key Laboratory of HighEfficiency and Clean Mechanical Manufacture,School of Mechanical Engineering,Shandong University， Jinan 250061,China; 2.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University，Jinan 250061,China; 3.Engineering Training Center, Shandong University, Jinan 250002, China
针对复杂背景下汽车板簧轮廓特征点难以提取的问题，提出改进麻雀搜索优化的Kmeans背景分割算法并辅以投射线激光提取待测特征点。首先，通过遍历全局像素，根据梯度阈值确定最优方向，减小向最优方向移动的步长数值，以此改进麻雀搜索优化算法，克服算法全局搜索能力弱、容易陷入局部最优的问题；其次，将麻雀搜索的感兴趣像素点作为Kmeans算法的初始中心点，把具有相似特征的像素点聚为一类，使板簧从复杂的背景环境中分割出来，获取板簧的外形轮廓；最后，向板簧表面投射线激光辅助标记，相交于板簧轮廓，提取待测特征点。结果表明，提出的基于背景分割的汽车板簧尺寸检测方法可以提取待测特征点，且精度可达025 mm，形成在线测量数据，有利于改进生产工艺。
Under complex background, it is difficult to extract the contour of automobile plate spring. Therefore, an improved Kmeans background segmentation algorithm based on sparrow search optimization is proposed, and the feature points to be measured are extracted by beam laser. Firstly, by traversing the global pixels, the optimal direction is determined according to the gradient threshold, and the step size moving to the optimal direction is reduced. So as to improve the sparrow search optimization algorithm, which can overcome the problem that the algorithm has weak global search ability and is easy to fall into the local optimal. Secondly, the pixel points of interest searched by sparrows were taken as the initial center point of Kmeans algorithm, and the pixels with similar characteristics were grouped into one group, so that the spring could be separated from the complex background environment and the contour of the spring could be obtained. Finally, ray laser is applied to the surface of the spring for auxiliary marking, and the feature points to be measured are extracted by intersecting the contour of the spring. The results show that the proposed detection method of plate spring size based on background segmentation can extract the feature points, and the accuracy can reach 025 mm, forming online measurement data, which is conducive to improving the production process.
周波,万熠,梁西昌,侯嘉瑞.基于麻雀搜索优化的背景分割算法的板簧尺寸检测方法[J].电子测量技术,2023,46(16):73-77
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